Although the ECG (electro cardiogram) is a universally accepted diagnostic method in cardiology, frequent mistakes are made in interpreting ECGs, because the most common approach for interpretation of ECGs is based on human memorization of waveforms, rather than using vector concepts and basic principles of electrocardiography (see Hurst, J. W., Clin. Cardiol. 2000 Jan.; 23(1):4-13).
Another problem with traditional ECG recordings is that the ECG may not provide adequate indications of electrical activity of certain regions of the heart, especially the posterior region.
The timing of cardiac electrical events, and the time intervals between two or more such events, has diagnostic and clinical importance. However, medical diagnosis and drug development has been significantly limited by the lack of adequate ECG measurement tools.
Furthermore, prior analysis of ECG recordings required a substantial amount of training and familiarity with reading of the recorded waveforms.
There have been many attempts to extract additional information from the standard 12-lead ECG measurement when measuring the electric potential distribution on the surface of the patient's body for diagnostic purposes. These attempts have included new methods of measured signal interpretation, either with or without introducing new measurement points, in addition to the standard 12-lead ECG points.
Vector ECG
VCG is the oldest approach that includes the improvement of a spatial aspect to the ECG (see Frank, E., An Accurate, Clinically Practical System For Spatial Vectorcardiography, Circulation 13: 737, May 1956). Like conventional ECG interpretation, VCG uses a dipole approximation of electrical heart activity. The dipole size and orientation are presented by a vector that continuously changes during the heartbeat cycle. Instead of presenting signal waveforms from the measurement points (waveforms), as it is the case with standard 12-lead ECGs, in VCG, the measurement points are positioned in such a way that three derived signals correspond to three orthogonal axes (X, Y, Z), and these signals are presented as projections of the vector hodograph onto three planes (frontal, sagittal, and horizontal). In this way, VCG represents a step towards spatial presentation of the signal, but the cardiologist's spatial imagination skills were still necessary to interpret the ECG signals, particularly the connection to the heart anatomy. Furthermore, a time-dependence aspect (i.e., the signal waveform) is lost with this procedure, and this aspect is very important for ECG interpretation. VCG introduces useful elements which cannot be found within the standard 12-lead ECG, however, the incomplete spatial presentation and loss of the time-dependence are major reasons why VCG, unlike ECG, has never been widely adopted, despite the fact that (in comparison to ECG) VCG can more often correctly diagnose cardiac problems, such as myocardial infarction.
Modifications of Vector ECG
There have been numerous attempts to overcome the drawbacks of the VCG method described above. These methods exploit the same signals as VCG (X, Y, Z), but their signal presentation is different than the VCG projection of the vector hodograph onto three planes:
“Polarcardiogram” uses Aitoff cartographic projections for the presentation of the three-dimensional vector hodographs (see Sada, T., et al., J. Electrocardiol. 1982; 15(3):259-64). “Spherocardiogram” adds information on the vector amplitude to the Aitoff projections, by drawing circles of variable radius (see Niederberger, M., et al., J. Electrocardiol. 1977; 10(4):341-6). “3D VCG” projects the hodograph onto one plane (see Morikawa, J., et al., Angiology, 1987; 38(6):449-56. “Four-dimensional ECG” is similar to “3D VCG,” but differs in that every heartbeat cycle is presented as a separate loop, where the time variable is superimposed on one of the spatial variables (see Morikawa, J., et al., Angiology, 1996; 47:1101-6.). “Chronotopocardiogram” displays a series of heart-activity time maps projected onto a sphere (see Titomir, L. I., et al., Int J Biomed Comput 1987; 20(4):275-82). None of these modifications of VCG been widely accepted in diagnostics, although they have some improvements over VCG.
Electrocardiographic Mapping
Electrocardiographic mapping is based on measuring signals from a number of measurement points on the patient's body. Signals are presented as maps of equipotential lines on the patient's torso (see McMechan, S. R., et al., J. Electrocardiol. 1995; 28 Suppl:184-90). This method provides significant information on the spatial dependence of electrocardiographic signals. The drawback of this method, however, is a prolonged measurement procedure in comparison to ECG, and a loose connection between the body potential map and heart anatomy.
Inverse epicardiac mapping includes different methods, all of which use the same signals for input data as those used in ECG mapping; and they are all based on numerically solving the so-called inverse problem of electrocardiography (see A. van Oosterom, Biomedizinisch Technik., vol. 42-E1, pp. 33-36, 1997). As a result, distributions of the electric potentials on the heart are obtained. These methods have not resulted in useful clinical devices.
Timing of Cardiac Electrical Events
Cardiac electrical activity can be detected at the body surface using an electrocardiograph (ECG), the most common manifestation of which is the standard 12-lead ECG. Typical ECG signals are shown in present FIG. 6. The P-wave 10 represents atrial depolarization. The QRS complex 20 represents depolarization of the ventricles, beginning with QRS onset (QRSon) and ending at J point 30. Ventricular repolarization begins during the QRS and extends through the end of the T-wave (Tend) 70. The ST segment 40 extends from J point 30 to onset of the T-wave 50 (Ton). T-wave 45 extends from Ton 50 to Tend 70. U waves (not shown) are present on some ECGs. When present, they merge with the end of the T-wave or immediately follow it.
Physiologically, the T-wave is the ECG manifestation of repolarization gradients, that is, disparities in degree of repolarization at a particular time point between different regions of the heart. It is likely that the T-wave originates primarily from transmural repolarization gradients. (See Yan and Antzelevitch Circulation 1998; 98:1928-1936; Antzelevitch, J. Cardiovasc Electrophysiol 2003; 14:1259-1272.) Apico-basal and anterior-posterior repolarization gradients may also contribute. (See Cohen I S, Giles W R, and Noble D Nature. 1976; 262:657-661.)
Transmural repolarization gradients arise because the heart's outer layer (epicardium) repolarizes quickly, the mid-myocardium repolarizes slowly, and the inner layer (endocardium) repolarizes in intermediate fashion. Referring to FIG. 6, during ST segment 40, all layers have partially repolarized to a more or less equal extent, and the ST segment is approximately isoelectric. T-wave 45 begins at Ton 50 when the epicardial layer moves toward resting potential ahead of the other two layers. At the peak of the T wave (Tpeak) 60, epicardial repolarization is complete and the transmural repolarization gradient is at its maximum. Subsequently, endocardial cells begin their movement towards resting potential, thereby narrowing the transmural gradient and initiating the downslope of the T wave. Finally, the M cells repolarize, accounting for the latter part of the T-wave downslope. The T wave is complete at Tend 70 when all layers are at resting potential and the transmural gradient is abolished.
The QT interval may be estimated from an ECG by measuring time from QRSon to Tend. Abnormalities in the QT interval often mark susceptibility to life-threatening arrhythmias. Such abnormalities may be associated with genetic abnormalities, various acquired cardiac abnormalities, electrolyte abnormalities, and certain prescription and non-prescription drugs.
An increasing number of drugs have been shown to prolong the QT interval and have been implicated as causes of arrhythmia. As a result, drug regulatory agencies are conducting increasingly detailed review of drug-induced abnormalities in cardiac electrical activity. The accuracy and precision of individual measurements is highly important for clinical diagnosis of heart disease and for evaluation of drug safety. Drug regulatory bodies worldwide now require detailed information regarding drug effects on cardiac intervals measured from ECG data. (See M. Malik, PACE 2004; 27:1659-1669; Guidance for Industry: E14 Clinical Evaluation of QT/QTc Interval Prolongation and Proarrhythmic Potential for Non-Antiarrhythmic Drugs, http://www.fda.gov/cder/guidance/6922fn1.pdf). Improved measurement accuracy and precision would reduce the risk of clinical error and the amount of resources required during drug development to meet regulatory requirements.
This is particularly true for QT interval measurement, but also affects determination of virtually all cardiac electrical events, for example onset of the QRS complex (QRSon), the J point, onset of the T wave (Ton), and U waves.
No computerized QT measurement algorithm has demonstrated a sufficient degree of reliability (see M. Malik, J. Electrocardiol 2004; 37: 25-33). Accordingly, the consensus opinion is that QT intervals should be measured manually by experienced observers. (See Anderson M, et al. Am Heart J 2002; 144:769-781.) Unfortunately, manual QT measurement is labor intensive and expensive and remains inherently inaccurate (see Malik et al., Brit Heart J 1994; 71: 386-390).
Problems in manual QT interval determination result in part from lead selection. Measured QT intervals can vary significantly depending upon the ECG lead selected for measurement. Another common problem is finding Tend. This is usually defined as the point at which the measured voltage returns to the isoelectric baseline. However, T-waves are often low-amplitude, morphologically abnormal, fused with a following U-wave, or obscured by noise. The same may apply to J-points, P-waves, U-waves and other important cardiac events.
Thus, accurate and reproducible procedures for cardiac interval measurement are urgently needed. Also needed are procedures that are rapid and simple, and do not require a high degree of medical training and experience to achieve accurate and reproducible results. Such procedures are needed for QT interval and other critical cardiac electrical events, such as QRSon, the J point, J-T interval, Ton, Ton-to-Tend interval, and the like.
Automated cardiac interval measurement tools have been faced with several issues, including the issue of which lead to use for cardiac interval determination. In response, methods based on an average complex or median complex has been developed. In these approaches, a single representative complex is derived from all available complexes in the ECG under evaluation. The methods for accomplishing vary, however all of them involve some sort of averaging and compensation for different RR intervals and other variations that every ECG typically has. However, these methods are limited by the fact that they do not assess the quality of a complex before it is used in the creation of the average or median complex—for example, overall variability, high-frequency artifacts such as electrical noise or muscle artifact, and low-frequency artifacts such as baseline wander. Thus, a method for selection of an optimal complex or set of complexes based on an objective set of criteria is a significant need for developing a reliable method for automating evaluation of ECGs and determination of cardiac intervals.